| Paper: | TH-A2.17 |
| Session: | Applications of Radiometry II |
| Time: | Thursday, March 29, 09:00 - 10:20 |
| Presentation: |
Poster
|
| Topic: |
Theory, physical principles and electromagnetic models: |
| Title: |
Antenna-weighted water-body correction for SMAP land brightness temperatures |
| Authors: |
Julian Chaubell; NASA Jet Propulsion Laboratory | | |
| | Simon Yueh; NASA Jet Propulsion Laboratory | | |
| | Steven Chan; NASA Jet Propulsion Laboratory | | |
| | R. Scott Dunbar; NASA Jet Propulsion Laboratory | | |
| | Jinzheng Peng; NASA Goddard Space Flight Center | | |
| | Dara Entekhabi; Massachusetts Institute of Technology | | |
| Abstract: |
The Soil Moisture Active Passive (SMAP) mission was designed to acquire and combine L-band radar and radiometer measurements for the estimation of soil moisture with 4% volumetric accuracy away from coastal regions. In regions near the coast or near inland bodies of water, the SMAP footprint contains land and water resulting in errors in the soil moisture estimation. The mixed land and water emissions lead to the brightness temperature underestimation and thus to the overestimation of soil moisture. The determination of the land and water brightness temperatures contributing to the sensor measurement not only will have a significant impact on the reduction of the soil moisture errors near coastal zones but also on the retrieval of other physical parameters provided by the high level SMAP products.
Several authors have addressed the retrieval of the mixed land and water brightness temperatures using different techniques to improve, for example, current standard Special Sensor Microwave Imager (SSM/I) products. The improved brightness temperature measurements obtained in this way were then used to improve, for example, sea ice concentration over coastal regions or wind speed over the Great Lakes. In this presented work, we applied a single measurement algorithm to separate the land and water contribution for the uncorrected SMAP measurements.
We will present the effort to extract the brightness temperature related to the land fraction or water fraction (depending on the center of the footprint location) from the affected SMAP measurements. We evaluate the performance of our algorithm using simulated data over several of Earth’s regions. We then show some results obtained using real data.
Over all, our algorithm performed very well. Results over simulated data show significant statistical improvements. Results over real data show that our algorithm eliminates the mixed brightness temperatures over coastal areas. Some anomalies (underestimation of water temperature in some areas, for example) are still being investigated. We suspect residual pointing errors might be the cause of some of those problems.
The new SMAP improved product is expected to be delivered on April 2018.
|
